import plotly
plotly.offline.init_notebook_mode()
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('_mpl-gallery')
# make data:
x = 0.5 + np.arange(8)
y = [4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0]
# plot
fig, ax = plt.subplots()
ax.bar(x, y, width=1, edgecolor="white", linewidth=0.7)
ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
plt.show()
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('_mpl-gallery-nogrid')
# make data
x = [1, 2, 3, 4]
colors = plt.get_cmap('Blues')(np.linspace(0.2, 0.7, len(x)))
# plot
fig, ax = plt.subplots()
ax.pie(x, colors=colors, radius=3, center=(4, 4),
wedgeprops={"linewidth": 1, "edgecolor": "white"}, frame=True)
ax.set(xlim=(0, 8), xticks=np.arange(1, 8),
ylim=(0, 8), yticks=np.arange(1, 8))
plt.show()
import seaborn as sns
sns.set_theme(style="darkgrid")
# Load an example dataset with long-form data
fmri = sns.load_dataset("fmri")
# Plot the responses for different events and regions
sns.lineplot(x="timepoint", y="signal",
hue="region", style="event",
data=fmri)
<Axes: xlabel='timepoint', ylabel='signal'>
import seaborn as sns
sns.set_theme(style="darkgrid")
df = sns.load_dataset("penguins")
sns.displot(
df, x="flipper_length_mm", col="species", row="sex",
binwidth=3, height=3, facet_kws=dict(margin_titles=True),
)
<seaborn.axisgrid.FacetGrid at 0x21b3434cc90>
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")
fig.show()
import plotly.express as px
df = px.data.tips()
fig = px.bar(df, x="sex", y="total_bill", color="smoker", barmode="group")
fig.show()
| Stretch/Untouched | ProbDistribution | Accuracy |
|---|---|---|
| Stretched | Gaussian | .843 |
from PIL import Image
from matplotlib import pyplot as plt
img = Image.open('C:/CSCN8010-Lab/Desk/tree.jpg')
fig = plt.imshow(img)
plt.axis('off')
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
px.scatter(x=[1, 2, 3], y=[1, 0, 3])